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https://issues.apache.org/jira/browse/SPARK-14761?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Bijay Kumar Pathak updated SPARK-14761:
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Comment: was deleted
(was: Hi [~joshrosen], how do we handle the on=None while passing to JVM api,
since passing None throws {{java.lang.NullPointerException}} in my regression
test.)
> PySpark DataFrame.join should reject invalid join methods even when join
> columns are not specified
> --------------------------------------------------------------------------------------------------
>
> Key: SPARK-14761
> URL: https://issues.apache.org/jira/browse/SPARK-14761
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Reporter: Josh Rosen
> Priority: Minor
> Labels: starter
>
> In PySpark, the following invalid DataFrame join will not result an error:
> {code}
> df1.join(df2, how='not-a-valid-join-type')
> {code}
> The signature for `join` is
> {code}
> def join(self, other, on=None, how=None):
> {code}
> and its code ends up completely skipping handling of the `how` parameter when
> `on` is `None`:
> {code}
> if on is not None and not isinstance(on, list):
> on = [on]
> if on is None or len(on) == 0:
> jdf = self._jdf.join(other._jdf)
> elif isinstance(on[0], basestring):
> if how is None:
> jdf = self._jdf.join(other._jdf, self._jseq(on), "inner")
> else:
> assert isinstance(how, basestring), "how should be basestring"
> jdf = self._jdf.join(other._jdf, self._jseq(on), how)
> else:
> {code}
> Given that this behavior can mask user errors (as in the above example), I
> think that we should refactor this to first process all arguments and then
> call the three-argument {{_.jdf.join}}. This would handle the above invalid
> example by passing all arguments to the JVM DataFrame for analysis.
> I'm not planning to work on this myself, so this bugfix (+ regression test!)
> is up for grabs in case someone else wants to do it.
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